app.py

import os
import json
from flask import Flask, request, jsonify, render_template
from groq import Groq
from dotenv import load_dotenv

load_dotenv()

app = Flask(__name__)

client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

GROQ_MODEL = "llama-3.1-8b-instant"


def build_prompt(text: str) -> str:
    # Prompt in inglese: i modelli Llama performano meglio in inglese
    # anche quando il testo da analizzare è in un'altra lingua.
    return f"""Analyze the following text and respond ONLY with a valid JSON object.
No markdown, no backticks, no additional text.
Required schema: {{"sentiment": "positive|negative|neutral",
"sentiment_score": float 0-1, "emotion": "joy|sadness|anger|fear|surprise|disgust",
"emotion_intensity": float 0-1, "keywords": [max 5 keywords],
"summary": "string max 20 words"}}
Text to analyze: {text}"""


def parse_groq_response(content: str) -> dict:
    # Il modello a volte aggiunge backtick markdown nonostante le istruzioni — li rimuoviamo
    cleaned = content.strip()
    if cleaned.startswith("```"):
        lines = cleaned.split("\n")
        cleaned = "\n".join(lines[1:-1] if lines[-1].strip() == "```" else lines[1:])

    result = json.loads(cleaned)

    required_fields = ["sentiment", "sentiment_score", "emotion", "emotion_intensity", "keywords", "summary"]
    for field in required_fields:
        if field not in result:
            raise ValueError(f"Campo mancante nella risposta AI: {field}")

    return result


@app.route("/")
def index():
    return render_template("index.html")


@app.route("/api/analyze", methods=["POST"])
def analyze():
    data = request.get_json(silent=True)
    if not data or "text" not in data:
        return jsonify({"error": "Parametro 'text' mancante nel body della richiesta"}), 400

    text = data["text"].strip()

    if len(text) < 10:
        return jsonify({"error": "Il testo deve contenere almeno 10 caratteri"}), 400

    if len(text) > 1000:
        return jsonify({"error": "Il testo non può superare 1000 caratteri"}), 400

    try:
        prompt = build_prompt(text)
        completion = client.chat.completions.create(
            model=GROQ_MODEL,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.1,
            max_tokens=300,
        )

        raw_content = completion.choices[0].message.content
        result = parse_groq_response(raw_content)

        return jsonify(result)

    except json.JSONDecodeError as e:
        return jsonify({"error": f"Risposta AI non valida (JSON malformato): {str(e)}"}), 500

    except ValueError as e:
        return jsonify({"error": str(e)}), 500

    except Exception as e:
        return jsonify({"error": f"Errore durante la chiamata alla Groq API: {str(e)}"}), 500


if __name__ == "__main__":
    print("MoodMapper avviato su http://localhost:5001")
    app.run(debug=True, port=5001)